A potential of the usage of optical coherence tomography (“OCT”) as a diagnostic procedure and technique which is capable of providing high-resolution cross-sectional images of a tissue microstructure to depths of, e.g., 2 mm has been well appreciated. However, in a number of clinical applications, the diagnostic utility of the conventional OCT techniques has been limited by a confounding effect of a speckle noise. This noise, which can be a large magnitude amplitude noise at the size scale of the imaging resolution, can be produced from a coherence ranging technique which may be used to provide a depth sectioning of the evaluated tissue. Certain clinically relevant structures, despite being larger in size than the ˜10 mm imaging resolution, may lack a sufficient intrinsic optical scattering contrast relative to the surrounding tissue to be clearly identified through this speckle noise.
The proposed approaches for mitigating the impact of speckle noise can be categorized as either physical compounding methods or digital processing methods. For example, the physical compounding methods generally function by combining multiple, speckle uncorrelated measurements of the same location in the analyzed tissue. The implementation of such methods may require modifications to the imaging system that can complicate a design of the catheter and the design of a minimally-invasive probe. Examples of these physical compounding methods can include angular compounding, frequency compounding, and polarization compounding (e.g., a polarization diversity detection). In contrast, the digital processing methods have conventionally been applied entirely to two-dimensional images using procedures or filters that aim to preferentially remove the speckle noise, while preserving certain features associated with the tissue structures. Such techniques include adaptive filtering, regularization, and wavelet denoising. However, the digital processing methods, unlike the compounding methods, are likely limited to the information content contained within the original speckled image. This, it is important for the digital processing methods to outperform the considerable ability of an experience implementer of the OCT to visually filter the noise and recognize the underlying tissue structures.
Such limitations, however, generally may not apply if the digital processing methods are extended to operate in three-dimensions on volumetric OCT datasets. Certain improvements in OCT imaging speeds have enabled the practical clinical implementation of the volumetric imaging using OCT methods and systems. Thus, there is now an underlying clinical motivation to employ such methods and systems as tools for a comprehensive disease screening. Since these three-dimensional datasets may not be directly visualized, a diagnosis may typically be rendered from one or more images sectioned from the dataset. Preferably, these sectioned images can incorporate measured information both from within the section plane (e.g., in-plane measurements) and from adjacent locations out of the sectioning plane (e.g., out-of-plane measurements).
Indeed, there may be a need to overcome at least some of the deficiencies associated with the conventional arrangements and methods described above. For example, this can be achieved by the volumetric filtering of the dataset prior to the sectioning thereof. Because this exemplary process can increase the information content of the resultant image through inclusion of out-of-plane measurements, substantial enhancement can be achieved.